Big Data Analytics has provided companies among various industries huge potentials for acquiring more business value. One key category of Data Analytics that is seeing a rise in use across multiple sectors is Predictive Analytics. Simply put, this type of analytics deals with transforming collected data into insights on what may happen in the future. This form of analytics goes beyond just describing situations, it seeks to accurately assess possible eventualities. Through the use of machine learning, and complex statistical and modeling techniques, future insights are generated with a significant degree of precision and speed. Although it is often used to predict trends and events that can happen in the future, Predictive Analytics can also be used to gain insights on past or present events.
Despite the fact that they both deal with possible eventualities, Predictive Data Analytics is different from Business Forecasting. The former is focused on predicting in detail, focusing on identifying future trends of specific business elements, while the latter casts a wider net, with a focus on holistic future outcomes for whole sectors. This “magnifying glass” property of Predictive Analytics lets companies and organizations target specific processes that can be improved to deliver greater value.
Successful implementation of Data Analytics involves the use of a cyclical process. This process allows for the continuous development and improvement of the whole analytics system, which will result in better insights that turn into actionable models that return more value.
The Predictive Analytics process starts of with planning a Data Analytics project. This initial part determines goals, possible outcomes, project scope. Most importantly, this is where variables and related data sets are identified. The next stage involves collecting data from various sources relevant to the specific area in which a business wishes to glean insights from. This sources could include web logs, click streams, usage sensors, among others. These data are then processed and formatted for analysis.
Data Analysis involves cleaning and modelling data. This is where the actual actionable insights are generated. Statistical algorithms also are put in play in this stage to validate conclusions, compare, and relate them to existing models. Once these are done, the insights are then transformed into new business models that improve certain elements or processes. These deployed models are finally monitored and evaluated to determine if they have improved the system or have delivered significant value returns.
These steps can be complex to implement and require certain specialities and expertise among the people who will work on them. Building a team to perform all the stages of the Data Analytics cycle can be a mammoth task in terms of time-to-initiate and resources. That is why smart businesses opted to engage with strategic partners who have the people and expertise to provide third-party services that can perform all the processes needed to perform a successful Predictive Analytics project.
Predicting business trends used to be a risky affair with traditional forecasting and statistical techniques. It involved a lot of slow and costly processes including manual research, market studies and customer field surveys. The results are also varied and sometimes, even unreliable. With the onset of Big Data Analytics, specifically Predictive Analysis, the process of gaining insights has been sped-up and streamlined, cutting the time between planning to deployment of models exponentially.
Predictive Analytics allows businesses to have a proactive view into trends with a lot more accuracy and reliability. This in turn, allows them to improve their business processes to save resources or gain new opportunities to earn more. This type of Data Analytics is used differently across industries. Some of its advantages in specific business sectors include
In Healthcare - The healthcare industry has taken on the leading role in adopting Data Analytics methods and techniques into their processes to deliver better medical services. Historical data sets involving patient/client behaviors regarding their use of healthcare resources, health-related behaviors, and hospitalization records can return insights regarding high risk individuals and fraudulent claims. Past data trends can also yield models that can be used to optimize medical resource allocations, scheduling and other risk factors. The industry as a whole is seen as further integrating greater levels of analytics in the future.
In Media - Predictive analytics have been used by media companies to create viral content that facilitate lead conversion or greater market share. These new content are patterned towards trends that have high engagement from consumers.
In Manufacturing - Historical data is utilized into predicting possible machine failures, allowing businesses to setup preemptive maintenance schedules to ensure continuity.
In Financial Services - Banks and lending corporations have used predictive analytics to generate credit risk models. Advanced analysis can also lead to these companies identifying key investments that can be improved or cut-off depending on their predicted profitability.
In E-commerce and Retail - Data gathered on customer buying habits have allowed e-commerce sites to feature certain products that have the potential to be more popular. Sales and discounts can also be set up through insights gained from predictive analytics.
Modelling techniques are usually specific to certain business processes but a few universal ones are often employed across multiple platforms. These techniques can be applied to a comprehensive array of business elements and can yield valuable information that can be made into new models that improve those areas. Some of these core modelling techniques include:
Regression - Techniques based on statistical regression are often applied on business processes involving complex quantitative data, such as those from banking and finance. Predictive analytics helps identify relationships and causality between variables, and helps identify trends in these relationships, like how increase in one variable can affect change in another at some future point in time.
Decisions Trees - The simplest and most widely used form of decision tree modelling is the A/B testing. Controlled deployment of two predicted models with key points of divergence and eventual further analysis results in identifying which models are superior. More complicated branching methods can identify possible outcomes of multiple decisions and how any point can affect those that are next in line.
Neural Networks - Emerging technologies that emulate human thinking processes have become the most efficient and fastest techniques available. These technique makes use of algorithms that are used to identify relationships between data sets. With the scalability options available, the amount of data and the speed at which data sets are processed have been accelerated to such levels never seen before. These innovations hold much potential in furthering predictive analytics to greater use and application
Predictive Analytics has a plethora of benefits for any type of business from any industry. Despite this, starting up Data Analytics within businesses remains a challenge to this day. This is brought about by a lack of or minimal business stakeholders’ buy-in or difficulties in finding the right people with the necessary skill sets to perform Big Data Analytics.
The best way to kick-start a predictive analytics project is to start small: focus on a critical area that can be improved. After that, look for strategic partnerships, such as experienced outsourcing Data Analytics firms that can help work with the company in the full analytics process, from planning to implementation. FilAm Software Technology partnered with Ecuiti, helps companies reduce time-to-market of new models and get value-return sooner. Establishing these relationships can save a lot of resources in terms of time and cost, that are otherwise wasted if analytics is done alone.